Second, recent research has posited that a 4-way classification is needed to capture responses seen in the unfolding of breaking news [1]}, [2]}. Moving away from the 2-way classification above, which is somewhat limited for our purposes, we adopt this expanded scheme including tweets that are supporting, denying, querying or commenting rumours. This adds two more categories to the scheme used in early work, where tweets would only support or deny a rumour. Moreover, our approach takes into account the interaction between users on social media, whether it is about appealing for more information in order to corroborate a rumourous post (querying) or to say something that does not contribute to the resolution of the rumour's veracity (commenting). Finally, instead of dealing with tweets as single units in isolation, we exploit the conversational structure of Twitter replies, building a classifier that learns the dynamics of stance in tree-structured conversational threads. The closest work when it comes to exploiting conversational structure in tweets is that of Ritter et al. ritter2010unsupervised who modelled linear sequences of replies in Twitter conversations with Hidden Markov Models for dialogue act tagging, but the structure of the tree as a whole was not exploited.
